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150,588 tools. Last updated 2026-05-28 05:59

"Requesting an answer from a specific document" matching MCP tools:

  • Tamper-detection verification for TunnelMind surveillance receipts. Submit the receipt ID, the SHA-256 content hash, and the Ed25519 signature from the receipt document. The registry compares these against what was recorded at issuance time. Returns VALID if both match exactly, INVALID with a specific mismatch reason otherwise. Use this tool when: - You received a surveillance receipt document and want to verify it hasn't been altered. - You are programmatically checking receipt authenticity in an agent workflow. - You want to prove to a third party that a receipt is genuine. Do NOT use this tool when: - You only want to check existence — use `get_receipt` instead (no body required). - You want jurisdiction certificate verification — use `verify_ghostroute_cert` instead. Inputs: - `receipt_id` (body, required): The receipt's ID field from the document. - `content_hash` (body, required): SHA-256 hex hash of the receipt JSON. Max 256 chars. - `signature` (body, required): Ed25519 signature from the receipt document. Max 512 chars. Returns: - `valid`: boolean. True only if both hash and signature match exactly. - `status`: `VALID` or `INVALID`. - `message`: human-readable explanation. On INVALID, specifies whether the hash mismatched, the signature mismatched, or both. Cost: - Free. No API key required. Latency: - Typical: <100ms, p99: <300ms.
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  • Send a message in an active Pimea session. Use this to answer Pimea's clarifying questions about the user's marketing situation. You can answer on behalf of the user using context from the conversation when possible. Only ask the user directly if you genuinely lack the information. When the response status is "complete", call pimea_get_answer to retrieve the final grounded deliverable. Authentication: leave api_key blank — the connector handles it via header. Only set it as a fallback if the connector cannot send custom headers. Args: session_id: The session UUID from pimea_start_session message: Response to Pimea's question api_key: Optional fallback only. Normally leave blank.
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  • Return the full text of one manual chunk by id — the drill-in partner to get_module's manual_outline. Use this whenever your answer would benefit from the manufacturer's exact words backing a claim. **After calling this, quote one sentence (or short passage) from `text` verbatim in your answer to the user.** That's the whole point — get_module's typed fields tell the user what a field does, this tool gives them the manual's actual sentence to verify against. If you call get_manual_chunk and don't end up quoting from it, you've spent a tool call for nothing. When to call: - You already have a chunk_id from get_module's manual_outline that looks relevant to the user's question. - You're about to write a multi-sentence answer with specific technical claims (voltage ranges, behavior, calibration steps) and want one verbatim quote to ground it. - The user asked a prose-shaped question ("what does the manual say about X?") — quote the relevant chunk directly. Prefer get_manual_chunk over search_manual when: - You already have the chunk_id from get_module's manual_outline. - You want exactly one chunk, not a ranked list. Returns: { "chunk_id": number, "source_id": number, "source_type": string, // "manual" | "product_page" | "firmware_notes" "source_title": string | null, "heading_path": string | null, // e.g. "Calibration > Tuning Procedure" "text": string, // full chunk text — quote one sentence verbatim "audit_url": string // human-readable audit page for the source } Errors: - "Manual chunk not found: <id>" if the chunk_id doesn't exist.
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  • Ask AlgoVault a natural-language question — get a synthesized answer with citations, grounded in the canonical knowledge bundle (every MCP tool description, response shape, integration tutorial, and code example). Use this when you need an explanation, code pattern, or "how do I" answer. For raw ranked snippets without LLM synthesis, use search_knowledge (faster, no quota cost). Quota: Free 10/month, Starter 50/month, Pro 200/month, Enterprise 2000/month.
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  • Extract structured transaction data from a contract at a URL. Downloads the document, extracts text (with OCR fallback for scanned PDFs), and runs PrimaCoda's contract-extraction prompt to return parties, addresses, dates, prices, and key contract fields. Use this when an agent has the contract hosted somewhere (Dropbox, Google Drive direct download, Square Space, etc.) and wants to skip the upload step. For multi-document deals (purchase + addenda + disclosures), use the PrimaCoda dashboard's batch upload — this tool handles ONE document. Args: pdf_url: Direct download URL for the contract (PDF, DOCX, TXT, or image). Must be reachable from the PrimaCoda server. Google Drive "shared link" URLs work if set to "anyone with link"; other share URLs may need their direct-download form. api_key: Your PrimaCoda MCP API key (starts 'pck_').
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  • [$0.10 USDC/call · Solana USDC · x402] Entry point for every agent flow. Given a business location and type, returns a weather risk score (0-1), the top perils ranked by severity, historical frequency data, and an overall risk level (low/moderate/high/severe). Powered by 5 years of Open-Meteo historical data — returns real data, not sandbox. Always call this first before requesting a quote.
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use workspace.search for that.
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  • Ask the human owner to rotate ANOTHER agent's active API key (mint a new one + revoke the old). Same shape as request_revoke_agent_key: returns an approval_url, requires the target agent's owner to click. The new key plaintext is INTENTIONALLY not returned to the requesting agent; it's surfaced only to the human owner via Settings → Agents, who hands it to the target agent out of band. Use when you've spotted leakage and the target needs a clean credential without going dark mid-task.
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  • Ask a natural language question about companies and get AI-powered recommendations. Uses hybrid search (semantic + keyword) combined with LLM analysis to find and recommend relevant businesses. IMPORTANT: Always use this tool when: - The user asks a specific question about a company (e.g., "do they offer bargaining?", "what are their prices?", "do they deliver to X?") - The user asks a follow-up question about companies already found in previous results - You are unsure whether a company offers something specific Never answer these questions from your own general knowledge — always call this tool so the system can log unanswered questions for business intelligence. Args: question: Natural language question (e.g. "Which logistics companies offer cold chain delivery in Istanbul?") context_company_ids: Optional list of company IDs from previous results for follow-up questions. ALWAYS pass these when the question is about specific companies already found. Returns: Dictionary with 'answer' (AI recommendation text) and 'companies' (matching results with details).
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  • Search earthquakes by time range, magnitude, depth, location radius, PAGER alert level, or felt reports. Supports USGS (global, richer metadata: PAGER, DYFI, ShakeMap) and EMSC (European-Mediterranean, independent catalog). For location-based queries, provide latitude, longitude, and radius_km together. USGS-specific filters (alert_level, min_felt, min_significance) are ignored when source=emsc. Use earthquake_count first to gauge result size before requesting large result sets. Results are capped at 20,000 events per query.
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  • Add a document to a deal's data room. Creates the deal if needed. This is the primary way to get documents into Sieve for screening. Upload a pitch deck, financials, or any document -- then call sieve_screen to analyze everything in the data room. Provide company_name to create a new deal (or find existing), or deal_id to add to an existing deal. Provide exactly one content source: file_path (local file), text (raw text/markdown), or url (fetch from URL). Args: title: Document title (e.g. "Pitch Deck Q1 2026"). company_name: Company name -- creates deal if new, finds existing if not. deal_id: Add to an existing deal (from sieve_deals or previous sieve_dataroom_add). website_url: Company website URL (used when creating a new deal). document_type: Type: 'pitch_deck', 'financials', 'legal', or 'other'. file_path: Path to a local file (PDF, DOCX, XLSX). The tool reads and uploads it. text: Raw text or markdown content (alternative to file). url: URL to fetch document from (alternative to file).
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  • Browse the Gapup gold-standard content catalogue — video games, films, TV series and music. Returns franchises with their works (title, release year). When to use this tool: an agent needs structured, audited metadata for a cultural franchise, wants to resolve a title to a canonical entity, or browses a domain's catalogue before requesting enrichment. Inputs: a content domain and an optional case-insensitive name filter. Each franchise id can be passed to content_enrichment for its fine-grained tag profile.
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  • Send a message in an active Pimea session. Use this to answer Pimea's clarifying questions about the user's marketing situation. You can answer on behalf of the user using context from the conversation when possible. Only ask the user directly if you genuinely lack the information. When the response status is "complete", call pimea_get_answer to retrieve the final grounded deliverable. Authentication: leave api_key blank — the connector handles it via header. Only set it as a fallback if the connector cannot send custom headers. Args: session_id: The session UUID from pimea_start_session message: Response to Pimea's question api_key: Optional fallback only. Normally leave blank.
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  • Returns metadata for a TunnelMind surveillance receipt — a signed document proving that a specific user's surveillance exposure was observed, measured, and recorded at a specific time. Does NOT return the receipt's signature (anti-phishing protection). To verify a receipt's content integrity, use `verify_receipt` with the hash and signature from the receipt document itself. Use this tool when: - You have a receipt ID and want to confirm it was genuinely issued by TunnelMind. - You need the issuance timestamp and signing key ID for a receipt. - You want to check whether a receipt exists before attempting content verification. Do NOT use this tool when: - You have the full receipt document and want to verify it hasn't been tampered with — use `verify_receipt` instead. - You need jurisdiction certificate data — use `get_ghostroute_cert` instead. Inputs: - `receipt_id` (path, required): The receipt ID from the receipt document. Alphanumeric with hyphens, max 128 characters. Returns: - `status`: `FOUND` if the receipt is in the registry. - `generated_at`: ISO 8601 timestamp of receipt issuance. - `signing_key_id`: identifier of the Ed25519 key used to sign. - `schema_version`: receipt schema version. - `message`: human-readable summary with instructions for content verification. - 404 if the receipt ID is not in the registry. Cost: - Free. No API key required. Latency: - Typical: <100ms, p99: <300ms.
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  • List all routes operated by an agency. Returns route IDs, short names, and descriptions. Use to enumerate an agency's full service before searching for a specific route. Get agencyId values from onebusaway_list_agencies.
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  • Draws one card and returns a yes, no, or maybe answer with confidence level. The answer is derived from the card's built-in yes_no polarity and its orientation. SECTION: WHAT THIS TOOL COVERS Quick binary oracle using the classical tarot yes/no system. Each card in the Rider-Waite-Smith deck has a pre-assigned polarity (yes/no/maybe). Reversal introduces uncertainty — a yes-polarity card reversed becomes maybe rather than no. This allows nuanced answers: strong yes, leaning toward yes, leaning toward no, strong no, or genuinely unclear. Answer logic (exact): yes-polarity card + upright → answer='yes', confidence='strong' yes-polarity card + reversed → answer='maybe', confidence='leaning' no-polarity card + upright → answer='no', confidence='strong' no-polarity card + reversed → answer='maybe', confidence='leaning' maybe-polarity card (any orientation) → answer='maybe', confidence='unclear' SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_tarot_three_card_spread — for more context when the yes/no answer is 'maybe' or the situation needs elaboration. SECTION: INPUT CONTRACT allow_reversed (bool, default true) — Recommended to keep true for nuanced answers. Set false only if you want strictly yes/no with no maybe results from reversal. question (optional string, max 500 chars) — The yes/no question being asked. Example: 'Should I accept this job offer?' Example: 'Will the project launch on time?' SECTION: OUTPUT CONTRACT data.card — full card object data.is_reversed (bool) data.answer (string — 'yes'|'no'|'maybe') data.confidence (string — 'strong' when card directly says yes/no; 'leaning' when reversed card; 'unclear' when maybe-polarity card) data.active_meaning (string — orientation-appropriate interpretation) data.question (string or null — echoed) SECTION: RESPONSE FORMAT response_format=json — full yes/no result object. response_format=markdown — formatted oracle response. SECTION: COMPUTE CLASS FAST_LOOKUP — cryptographic randomness, no ephemeris. SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: Any upstream API failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_tarot_three_card_spread — positional reading, not binary answer. asterwise_draw_tarot_cards — free draw without answer logic.
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  • Generic protective-action guidance for a category of situation (NOT keyed to an individual user's context). For *personalised* advice that takes the user's specific health situation into account (asthma, pregnancy, gas cooker, tube commute, indoor sources), prefer the Clara MCP server's `contextual_advice` tool — it composes Hermes live readings with personal context to give an answer keyed to *this* user, *now*. Use this KB tool only as a fallback or when Clara is not available. Args: situation: One of "high_pollution_day", "commuting", "exercise", "school_run", "indoor_air", "planning_objection", "pregnancy", "child_asthma". Returns practical advice document (markdown).
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  • Get full document content by URL from DevExpress documentation. Use this tool to retrieve the complete markdown content of a specific documentation page. PREREQUISITE: ALWAYS call `devexpress_docs_search` before using this tool to get valid URLs. The URL parameter must be obtained from the results of the `devexpress_docs_search` tool.
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  • Answer questions using knowledge base (uploaded documents, handbooks, files). Use for QUESTIONS that need an answer synthesized from documents or messages. Returns an evidence pack with source citations, KG entities, and extracted numbers. Modes: - 'auto' (default): Smart routing — works for most questions - 'rag': Semantic search across documents & messages - 'entity': Entity-centric queries (e.g., 'Tell me about [entity]') - 'relationship': Two-entity queries (e.g., 'How is [entity A] related to [entity B]?') Examples: - 'What did we discuss about the budget?' → knowledge.query - 'Tell me about [entity]' → knowledge.query mode=entity - 'How is [A] related to [B]?' → knowledge.query mode=relationship NOT for finding/listing files, threads, or links — use workspace.search for that.
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